Apparatus and method for integrated circuit forensics

ABSTRACT

A test system including an embodiment having a sensor array adapted to test one or more devices under test in learning modes as well as evaluation modes. An exemplary test system can collect a variety of test data as a part of a machine learning system associated with known-good samples. Data collected by the machine learning system can be used to calculate probabilities that devices under test in an evaluation mode meet a condition of interest based on multiple testing and sensor modalities. Learning phases or modes can be switched on before, during, or after evaluation mode sequencing to improve or adjust machine learning system capabilities to determine probabilities associated with different types of conditions of interest. Multiple permutations of probabilities can collectively be used to determine an overall probability of a condition of interest which has a variety of attributes.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional continuation of and claimspriority to U.S. patent application Ser. No. 14/313,360, filed on Jun.24, 2014, entitled “Apparatus and Method for Integrated CircuitForensics” which claims priority to U.S. Provisional Patent ApplicationSer. No. 61/838,532, filed Jun. 24, 2013, entitled “Apparatus and Methodfor Integrated Circuit Forensics,” the disclosures of which areexpressly incorporated by reference herein.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The invention described herein was made in the performance of officialduties by an employee of the Department of the Navy and may bemanufactured, used and licensed by or for the United States Governmentfor any governmental purpose without payment of any royalties thereon.This invention (Navy Case 200,338) is assigned to the United StatesGovernment and is available for licensing for commercial purposes.Licensing and technical inquiries may be directed to the TechnologyTransfer Office, Naval Surface Warfare Center Crane, email:Cran_CTO@navy.mil.

BACKGROUND AND SUMMARY OF THE INVENTION

The present invention relates sensing and detection of electrical orother characteristics associated with defective or unauthorized items ina supply chain using multiple detection and data system modalities.Defects or unauthorized status can include parts that do not conform totheir specifications, are not authorized by an original equipmentmanufacturer, a case where a used part is being passed off as a newpart, or a case where a part or component has been subjected to one ormore damage or stress events exceeding acceptable limit such aselectrostatic discharge (ESD) events. System defect or supply chainproblem detection is increasingly more difficult given large volumes,difficulty in accessing parts in an assembly, and different sizes,shapes, and input/output structure, particularly for mass produced partsor defect detection for parts that have left a factory. Thus, there is aneed to improve electronic system supply chain defect detectioncapabilities which can be used at any stage in a supply chain.

A common problem with existing methods of acquisition and comparison ofparts in a supply chain is that they are generally not good ataccounting for normal manufacturing process variations, which can varywith device lots and foundries. Existing methods also tend to focus on asingle stress indicator, such as input/output (I/O) shift due toelectrostatic discharge (ESD). Thus they do not represent comprehensiveevaluation methods.

One embodiment of the invention uses multiple test detection and datacollection/input modes coupled with one or more decision engines such asneural networks, image recognition, statistical correlation tools, anddecision trees, which can incorporate various learning processes.Another embodiment can also include a data collection system with oneembodiment including electromagnetic (EM) sensors and data collectioninputs adapted to sense test data and input the data to an embodiment ofthe multiple mode analysis decision engine to evaluate a device undertest (DUT) system. For example, an embodiment of the invention canincorporate integration of multiple EM sensors as well as data inputsand in synchronization with DUT stimulation for the purpose of producingdevice unique EM signatures accompanied by a decision engine, includinga neural engine, to provide a variety of novel embodiments of theinvention to meeting a variety of supply chain item defect orunauthorized item detection needs.

An exemplary embodiment can apply a decision engine to multipleelectrical characteristic modalities data sets for the purpose ofdetermining a probability that a microelectronic device is unauthorized,does not meet specification(s), or is defective. Inputs to an exemplarydecision engine can include a variety of potential data sets that can beevaluated. The additional information obtained in applying multiple datasets in combination with a sensor system that can be used with a widevariety of DUTs, both in a factory and elsewhere, will allow a much moreaccurate probability assessment of DUTs. Testing systems can also usevarious methods for measuring different stressors that would indicate apart has, for example, been previously used or stressed (thus isunacceptable or does not meet specification(s)), such as experiencing anESD damage event.

An exemplary stimulus could be applied in such a way as to producedevice dependent signatures useful in determining a probability that adevice has a defect, improper part installed, or has otherwiseexperienced environmental stress. An exemplary EM apparatus may includea positioning system, switch matrix, power combiner, switch andelectromagnetic interference (EMI) shielding to minimize stray EMIsignals. An exemplary embodiment can also combine various probe types,such as E-field, and H-field probes of varying bandwidths, as well asvisual, infra-red, etc in an integrated manner.

Additional features and advantages of the present invention will becomeapparent to those skilled in the art upon consideration of the followingdetailed description of the illustrative embodiment exemplifying thebest mode of carrying out the invention as presently perceived.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description of the drawings particularly refers to theaccompanying figures in which:

FIG. 1 shows a block diagram with a decision engine with multiplecapabilities along with possible inputs to the decision engine and anexemplary output in accordance with one embodiment of the invention;

FIG. 2 shows an exemplary schematic diagram of one aspect of one exampleembodiment of the invention;

FIG. 3 shows a learning phase for ESD stress in accordance with oneembodiment of the invention;

FIG. 4 shows a learning system adapted for use in testing associatedwith ageing of electronics or other parts in accordance with oneembodiment of the invention;

FIG. 5 shows an exemplary evaluation in accordance with one embodimentof the invention; and

FIGS. 6A and 6B show an exemplary processing sequence in accordance withone embodiment of the invention.

DETAILED DESCRIPTION OF THE DRAWINGS

The embodiments of the invention described herein are not intended to beexhaustive or to limit the invention to precise forms disclosed. Rather,the embodiments selected for description have been chosen to enable oneskilled in the art to practice the invention.

One aspect of the invention can include use of a decision engine toevaluate multiple electrical characteristic modalities and data sets forthe purpose of determining a probability that a DUT, e.g., amicroelectronic device, is an unauthorized, counterfeit, damaged,non-conforming to specification(s), or a defective item. Referring toFIG. 1, a conceptual block diagram is shown displaying some potentialdata sets (e.g., 11, 13, 15, 17, 19, 21, 23, 25) that can be evaluatedby an embodiment of the invention. Application of multiple data sets canenable a high accuracy probability determination of a particularcondition or status associated with a DUT such as discussed above.Various methods measure different aspects of a DUT which can becorrelated. For example, certain indicators associated with damageevents, e.g., stressors, that indicate a DUT or part has, for example,been previously used or stressed, such as previously experiencing an ESDevent.

Referring to FIG. 1, some exemplary data inputs used with one aspect ofan exemplary embodiment of the invention are shown. Power signature data(PSD) 11 can include data captured on an oscilloscope which shows DUToperational current vs time. PSD 11 can be taken under variousconditions including in synchronization with DUT stimulation fromautomatic test equipment (ATE). Output PSD 11 can be an electricalcurrent signature.

Exemplary embodiments can include output from E-field or H-field probeswhich measure EM emissions from a DUT. Electromagnetic Signature Data(EMSD) 13 can be taken under various conditions including insynchronization with DUT stimulation from ATE. EMSD 13 exemplary outputcan include a field emission map in a frequency or time domain.

Thermal Signature Data (TSD) 15 can be produced by an infrared (IR)imager that captures an IR image of a DUT. TSD 15 can be taken undervarious conditions including in synchronization with DUT stimulationfrom ATE. TSD 15 output can include a thermal map of a DUT surface.

Specification (Spec) Electrical Test Data (SETD) 17 can be produced ordetermined based on, for example, benchmark testing or amanufacturer(s)' data sheet. SETD 17 based test data output can includecreation of, e.g., an ASCII data file containing DUT test results pertest per pin for a DUT which is then compared with SETD 17 associatedwith a genuine, authorized, or undamaged baseline comparison DUT. SETD17 for a genuine, authorized, or undamaged baseline or comparison DUTcan be created by applying a predetermined plurality of inputs (e.g.,benchmark testing, or manufacturer data or data sheet) to the genuine,authorized, or undamaged DUT with a SETD 17 data set.

Initial Settings Data (ISD) 19 can include data initially read from aDUT. ISD 19 could take the form of user data in an EEPROM or user ID orsecurity bits set. For new parts, some or all data/setting associatedwith ISD 19 can be factory default settings.

Current vs. Voltage (IV) Curve (IVC) data 21. Traditional IV curveforces or injects a voltage and measures a resulting current. IVC data21 can be obtained with an embodiment that may incorporate ATE oranother embodiment can include a dedicated automated tester for ESDdetection.

Pulse Response Data (PRD) 23 can be obtained by one embodiment which caninclude an ESD tester which can apply a pulse for measurement on one ormore EM probes or with an oscilloscope providing per pin pulse response.PRD 23 can include an exemplary output including a frequency or timedomain waveform or frequency map.

Joint Test Action Group (JTAG) Electrical Test Data (JTAGETD) 25. JTAGis the common name for the IEEE 1149.1 Standard Test Access Port andBoundary-Scan Architecture. JTAGETD 25 can be used for testing printedcircuit boards and internal DUT testing such as logic built in self-test(LBIST). JTAGETD 25 can be used to set or read levels on I/O pins viaboundary scan, initiate LBIST or program internal memory. JTAGETD 25output can include an embodiment with a test signature.

Input data, such as discussed above and shown in FIG. 1, can be inputinto a Decision Engine 31 (e.g., neural networks, image recognition,statistical tools, and/or decision trees) to calculate on OverallProbability 33 that a DUT, e.g., a microelectronic device, is anunauthorized, counterfeit, damaged, non-conforming to specification(s),or a defective item.

FIG. 2 shows an exemplary schematic diagram of one aspect of one exampleembodiment of the invention. A DUT Testing Assembly 41 is shown whichincludes a support fixture 43 which supports or positions EM sensors,e.g. EM probes, 45 positioned over a DUT 47. Signal paths 49 connect EMsensors 45 with amplifiers 51. Amplifiers 51 are coupled with a SignalAnalysis Section 55 which provides signal analysis in a time domainand/or a frequency domain. For example, amplifiers 51 can be coupledwith a Signal Analysis Section 55 comprising a signal analyzer 57 and anoscilloscope 59 via a switch matrix 53. Separate connections (not shown)to the Signal Analysis Section 55 can be used or a summing section 61can be used which combines output from one or more amplifiers into acomposite signal for input into the Signal Analysis Section 55. A switch63 can be interposed between the Signal Analysis Section 55 and thesumming section 61. The EM sensors 45 can be adapted to berepositionable or movable to be placed over specific areas of interestof a particular DUT 47.

One embodiment of the invention can include armatures (not shown) foruse with an exemplary embodiment, e.g., a FIG. 2 system, to position anexemplary EM sensor 45 over areas of interest on a DUT 47. An exemplaryembodiment can include servos or mechanisms to move the EM sensors 45over a DUT 47 for repeatable measurements to include multiple differentidentical DUTs 47 or multiple measurements including measurements inmultiple positions relative to a DUT 47.

An exemplary embodiment of a DUT Testing Assembly 41 can include amultiplexer or switching system to permit selection of a single or anycombination of EM sensors 45. A multiplexer can provide an ability todynamically combine different EM sensors serving as array elements,minimizing signal acquisition time and quantity of data, whilemaintaining richness of signature information. A multiplexer can alsoperform a function of a switch matrix 53 such as in FIG. 2.

A power combiner may be used to perform a function of a summing section61. Such a power combiner would enable combination of signals selectedby the multiplexer in a desirable manner e.g., to be combined in amanner maintaining 50 ohm impedance.

A plurality of EM sensors 45 can be formed into an array configurationto detect particular EM emissions such as a particular EM emissionpattern from a particular set of components on a DUT 47 forming an EMsignature pattern.

An embodiment of the invention can include multiple types of EM sensors.For example, the plurality of EM sensors 45 can include combinations ofE-field and H-field sensors of various bandwidths. An embodiment of theinvention using an array allows optimizing signal quality for a giventechnology and acquisition environment.

An embodiment of the invention can also include a DUT Control System 64adapted to input a Known Good (KG) DUT Test Pattern Control Signals(KGDUTTPCS) (not shown) into a KG DUT 47 in order to stimulate the KGDUT 47 to produce signal characteristics to include a KG EM SignatureProfile (KGEMSP) for the KG DUT 47. KGEMSP data can include some or allof the data shown in FIG. 1. At least one KGEMSP is acquired by thearray of EM Sensors 45 which are positioned in a KG DUT EM SensorPosition (KGDUTEMSP) then stored for later comparison as a First EMSignal Pattern or KGEMSP. The DUT Testing Assembly 41 can then beconfigured to receive a second DUT, including components found in thefirst or KG DUT having a relative same or similar physicalconfiguration. The EM Sensors 45 array can then be repositioned tosubstantially match the first EM Sensors 45 array pattern based onstored KGDUTEMSP associated with the first or KG DUT 47; then the DUTTesting Assembly 41 and DUT Control System 64 next stimulates the secondDUT 47′(not shown) using the KGDUTTPCS associated with the KG DUT 47.The second DUT 47′ then produces a second or Under-Test (UT) EMSignature Pattern (UTEMSP) which is then acquired by the array of EMsensors 45 and stored as the second or UTEMSP. The First and Second EMSignature Patterns (KGEMSP and UTEMSP) are then compared and adetermination of whether or not the second DUT 47′ is an acceptable DUTor unacceptable DUT; where an acceptable DUT determination is made wherea substantial match between the First and Second EM Signature Patternindicates the Second DUT 47′ is a good DUT and a significant mismatchbetween the first and second EM signal pattern indicates the second DUT47′ is a defective DUT.

The DUT Control System 64 can also include an ability to store KG DUT 47configuration identification data and associated EM Signature Patternsfor KG DUTs (e.g., KGEMSP). Such DUT configuration identification data,including some or all data described in relation to FIG. 1, can includeoptically or electrically detectable patterns which can be associatedwith a KG DUT 7 and its stored KGEMSP as well as EM Sensor 45 arrayconfigurations/positions and KGDUTTPC used to generate the known-goodDUT's KGDUTTPC.

An embodiment of the DUT Control System 64 can also be adapted to couplewith the Signal Analysis Section 55 to receive outputs of the SignalAnalysis Section 55 and also to control EM sensor 45 positions and alsoto control devices or circuits positioned between EM Sensors 45 and theSignal Analysis Section 55. An embodiment of the DUT Control System 64can also include a storage medium adapted to store and output aplurality of machine readable instructions adapted to control variousaspects of the invention including the DUT Control System 64 and DUTTesting Assembly 41 as well as providing for an output capabilityincluding a user interface.

An exemplary user interface can include a graphical user interface (GUI)(not shown) which can provide a graphical depiction of circuit behavior,EM Signature Pattern comparison or overlays showing differences or nodifferences in detected EM signature patterns (e.g., comparison betweenthe first and second EM Signature Patterns) as well as a graphicalindication of portions of a second DUT which are producing anon-matching EM Signature. Data, such as shown in related to FIG. 1, canalso be displayed along with correlations of different DUT dataincluding some or all of the data shown in FIG. 1. A user interface canalso store data structures with selected test information to include EMSignature Pattern Data, mismatch data, and second or DUT 47characteristic comparison with a DUT 47′ for match, identification,and/or probability determination.

The DUT Control System 64 can also include a plurality ofmachine-implemented processing instructions stored on a digitalrecording media or other media such as a programmable logic structure toprovide additional analytical processing such as a determination ofprobability of defects associated with a second DUT 47′. A plurality ofinputs can also be provided to the DUT Control System 64 to permit awide variety of KGDUTTPCS to include power signatures, EM signatures,thermal signatures, specific electrical test inputs, initial settings ona second DUT 47′, electrostatic discharge (ESD), different input poweror signal curves, pulse responses, or specific standard electrical testsas well as some or all of the input types or data show in FIG. 1.Additional sensors can be added to an embodiment of the invention toinclude thermal sensors which create a KG thermal sensor pattern whichis then matched against a DUT 47′ thermal sensor output afterapplication of one or more KGDUTTPCS. Image recognition software can beincluded in another embodiment of the invention to permit matching ofthermal pictures or images of a KG DUT 47 with a second DUT 47′ todetermine good or no-good DUT determinations.

Processes and apparatuses incorporate a learning phase approach, bothinitial and during supply chain testing, in combination with amulti-modal test system can be provided to produce different types oftest data for input and processing with different types of decisionengines. Multi-modality electrical test data set evaluation based on amachine learning decision engine can be used to enable detection ofcounterfeit, unauthorized, undesirable, nonconforming, damaged, aged,and/or environmental stressed devices. An embodiment of the inventioncan produce probabilities that an engineer can take into account alongwith non-electrical based factors to help determine the likelihood thata given part is counterfeit, unauthorized, undesirable, nonconforming,damaged, aged, and/or environmentally stressed. An initial test can bedone to compare a known-good article or to test set of similar types oftype of electrical component testing apparatus can be positioned

FIG. 3 shows a learning phase for ESD stress. For IV curve signatures,the primary indicator of ESD induces stress; a similar devicemanufactured in the same technology with similar I/O structures can beused for the learning phase. A system, such as described herein, can beadapted to induce ESD stress and measure effects on a DUT 47′. The dataof the measured effects on a DUT 47′ can then be recorded and providedto the system. Different types of ESD stress can be induced. A varietyof ESD related stress tests can be used with this aspect of theinvention. For example, an escalating series of voltage or electricaldischarges can be applied or exposed to a known-good DUT such as, e.g.,a baseline or non-stress input then 250 volts, 500 volts, 750 volts, etcwhich are measured by a testing system, such as described above, withdata input into an analysis system, which could include a decisionengine (including, e.g., a neural network), during a stress testlearning phase. The decision engine could then store test output resultsand then use the stored results along with decision logic, e.g.,artificial intelligence and/or neural networks, to evaluate DUTs in asupply chain scanning system.

FIG. 4 shows a learning system adapted for use in testing associatedwith ageing of electronics or other parts in accordance with oneembodiment of the invention. For example, accelerated life burn-inprocesses can be used to age a part or DUT. At predetermined times basedon the technology and the burn-in environment, the part or DUT isretested to provide data for that equivalent age. For example, anescalating series of aging-effect-producing processes (e.g., newknown-good (baseline), five years, 10 years, 15 years, etc) can beapplied or exposed to a known-good DUT which is measured by a testingsystem, such as described above, with data input into an analysissystem, which could include a decision engine (including, e.g., a neuralnetwork), during an ageing test learning phase. The exemplary decisionengine could then store test output results and then use the storedresults along with decision logic, e.g., artificial intelligence and/orneural networks, to evaluate DUTs in a supply chain scanning system.Accelerated life test of sacrificed parts with unknown pedigrees couldprovide data pertaining to the remaining life for that particulardevice. While not as good as an ideal known-good device, such testingcan be useful to determine remaining life for reliability purposes.

FIG. 5 shows an exemplary evaluation in accordance with one embodimentof the invention such as for a given data set type (e.g., an IV curve).A variety of devices, parts, or DUTs can be tested using a system suchas described herein based on information obtained during learningphases, such as described above. Testing information or data is fed intoa decision engine having a machine learning system, such as a neuralnetwork, and then a variety of outputs can be produced in view of adesired probability or condition. Example probabilities can includeprobabilities relative to a baseline and specific condition categoriessuch as ESD and/or ageing. One embodiment can also test for acombination of conditions which have been shown to correlate with acondition of interest such as whether a DUT is genuine, counterfeit,damaged, tampered, or from a specific unauthorized source wherecorrelation of the combination of factors increases confidence in aparticular probability determination. Exemplary output can include acondition (such as an energy dispersive x-ray spectrometry (EDS))equivalent to 250V or device age equivalent to 5 years of use) alongwith an associated probability of a part or DUT meeting a condition ofinterest such as counterfeit, genuine, damaged, aged, non-conforming toa specification, etc. An exemplary probability will rarely be a 100%good/bad type number because the data contain noise, but moreimportantly a condition of a real counterfeit or category of a conditionof interest will almost never be directly equivalent to the condition ofthe device used to train a learning system such as described herein. Forexample, a system might be trained for ESD stress using a sequence suchas; 1) baseline, 2) 250V, 3) 500V, 4) 750V and 5) 1000V, while thecounterfeit device being evaluated might have experienced an ESD eventof 675V. In this example the probability would be greater for 750V, butnot a direct correlation.

In this example, a number of data sets used can vary from device type todevice type and also based on available resources. Once all or some datasets have been individually evaluated they are combined for evaluationas shown in FIG. 1 to calculate Overall Probability 33. This step canuse neural nets and/or a decision tree based on the technology, numberand types of data sets that were applied.

FIGS. 6A and 6B show an exemplary processing sequence in accordance withone embodiment of the invention. At Step 1: position a test assemblycomprising a plurality of EM sensors; At Step 2: position a known-goodDUT relative to the test assembly; At Step 3: position the plurality ofEM sensors at a plurality of locations in relation to DUT in a firstsensor configuration; At Step 4: selectively energize the DUT to producea first EM emission pattern from a plurality of sections on the DUT,where selective energization includes inputs associated with a teststimulus patterns adapted to enhance or create a detectable EMsignature; At Step 5: acquire the first EM emission pattern producedfrom Step 4 by using the plurality of EM sensors; at Step 6: store thefirst EM emission pattern; At Step 7: remove the known-good DUT andreplace with a second DUT; At Step 8: position the second DUT relativeto the test assembly; At Step 9: position the plurality of EM sensors atthe plurality of locations in relation to DUT at the first sensorconfiguration; At Step 10: selectively energize the second DUT toproduce a second EM emission pattern from a plurality of sections on thesecond DUT; At Step 11: acquire the second EM emission pattern producedfrom Step 10 by using said plurality of EM sensors at said first sensorconfiguration; At Step 12: store the second EM emission pattern; At Step13: compare the first and second EM emission pattern; At Step 14:determine if the first and second EM emission patterns are substantiallyidentical or different; At Step 15: identify the second DUT asacceptable if the first and second EM emission patterns match orunacceptable if the first and second EM emission patterns do not match.

One advantage of one embodiment of the invention includes providing anability for users to implement an optimal design for a selected ortarget technology and permit rapid evaluation by creating a testingassembly, e.g., printed circuit board, with only sensor array elements,position of such elements and signal inputs for a control mechanismneeding to be modified.

Although the invention has been described in detail with reference tocertain preferred embodiments, variations and modifications exist withinthe spirit and scope of the invention as described and are defined inthe following claims.

The invention claimed is:
 1. An apparatus for testing includingmeasuring electromagnetic emissions and determining defective componentscomprising: a sensor array comprising a plurality of sensors adapted tobe moveable; a signal analysis section comprising a section comprising atime domain and signal domain signal analysis signal section; a deviceunder test (DUT) holder adapted to hold and position a first and secondDUT relative to the sensor array; a DUT stress application sectionadapted to generate one or more DUT stress conditions to said first DUTwhich correlate with life cycle age or a life cycle reduction eventassociated with said DUT, where said DUT stress condition associatedwith life cycle age is applied by an escalating series of burn-in orheating conditions applied to said first DUT, said DUT life cyclereduction event comprises electrostatic discharge application to atleast one section of said first DUT; a control mechanism adapted toindependently position elements of said sensor array relative to areasof interest on said first and second DUT based on a first positioninput; a DUT control section comprising a machine readable storagemedium adapted to receive and store a plurality of machine readableinstructions operable to control said apparatus and said first andsecond DUTs, said machine readable storage medium further comprises afirst plurality of machine readable instructions adapted to operate saidcontrol mechanism in order to acquire and store a plurality of firstsensor array signature data associated with said sensor array outputsfrom said first DUT based on a first plurality of test signal controlinputs to said first DUT and said first position input while one or moresaid DUT stress conditions are applied to said first DUT, wherein eachof said plurality of said first sensor array signature data isrespectively associated with said one or more DUT stress conditions;wherein said DUT control section further comprises a second plurality ofmachine readable instructions stored on said machine readable storagemedium adapted to stimulate said second DUT when said second DUT isplaced in said DUT holder with said first plurality of test signalcontrol inputs, said DUT control section further comprises a thirdplurality of machine readable instructions stored on said machinereadable storage medium adapted to acquire a plurality of second sensorarray signature data associated with said sensor array outputs from saidsecond DUT based on said first plurality of test signal control inputsto said second DUT and said first position input; wherein said DUTcontrol section further comprises a fourth plurality of machine readableinstructions stored on said machine readable storage medium adapted tomatch said first and second sensor array signature data associatedrespectively with said first and second DUT, wherein a substantial matchof said signature data indicates a first condition associated with saidsecond DUT and a non-match indicates a second condition associated withsaid second DUT; an input and output section adapted to interact withsaid DUT control section, said input and output section comprising auser interface including a graphical user interface adapted to displayan indication of said first or second condition associated with saidsecond DUT.
 2. An apparatus as in claim 1, wherein said first conditioncomprises a DUT acceptable data indicator and said second conditioncomprises a DUT unacceptable data indicator.
 3. An apparatus as in claim1, wherein said fourth plurality of machine readable instructionscomprises a decision engine and rule base comprising a plurality of datasets associated with a plurality of conditions comprising said first andsecond conditions, said decision engine and said rule base are operablefor determining a probability that said second DUT is associated withone or more of said plurality of conditions, wherein said firstcondition comprises an authorized or meets specification condition andsaid second condition comprises an unauthorized, does not meetspecification, or is defective condition.
 4. A testing system includinga system for measuring electromagnetic emissions and determiningdefective components comprising: a sensor system means adapted to testone or more devices under test (DUT) in learning as well as evaluationmodes, said sensor system means is adapted to collect a variety of testdata as a part of a machine learning system associated with a known-goodsaid one or more DUT samples subjected to simulated or generatedcondition of interests comprising stimulation of sections of saidknown-good DUT and application of one or more stress events to saidknown-good DUT sample, said data collected by the machine learningsystem is operable to calculate probabilities data that an unknown-goodDUT in an evaluation mode substantially matches a condition of interestcomprising a DUT acceptance data or a DUT rejection data; and an inputsection and an output section adapted to output said probabilities dataassociated with said unknown-good DUT; wherein learning modes areswitched on before, during, or after evaluation mode sequencing toimprove or adjust machine learning system capabilities to determine saidprobabilities associated with different types of said conditions ofinterest wherein said system are adapted to determine multiplepermutations of said probabilities that are collectively used todetermine an overall probability of one or more said conditions ofinterest which has a variety of attributes associated with one or moresaid DUT acceptance data and DUT rejection data.
 5. A system as in claim4, wherein said learning and evaluation system include an artificialintelligence system comprising a neural network or a decision enginecomprising a plurality of rules associated with generating saidprobabilities data.
 6. A system as in claim 4, wherein said learning andevaluation system are adapted for determining a probability that DUTcomprising a microelectronic device is unauthorized, does not meetspecification(s), or is defective.
 7. A testing system comprising: aplurality of device under test (DUT) measurement and datacollection/input sections comprising electromagnetic (EM) spectrumsensors and data collection sections adapted to apply a plurality ofstimulation inputs to a plurality of points on a known-good and anunknown-good DUT and respectively sense a first and second plurality oftest data from said known-good and unknown good DUTs, wherein saidplurality of stimulation inputs applied to said known-good DUT isapplied during synchronized application of one or more life cyclesimulation stress environments are applied to said known-good DUTcomprising one or more of a group comprising electrostatic stress andageing events comprising burn-in or heating of said known-good DUTassociated with operational or functional capability conditions of saidknown-good DUT during specific points of said known-good DUT's servicelife; one or more decision engine sections respectively associated withsaid plurality of DUT measurement and data collection/input sections,said one or more decision engine sections comprising a neural network,image recognition, statistical correlation section, and decision treesection, said one or more decision engines adapted to operate said DUTin a learning mode and an evaluating mode, said one or more decisionengines are further adapted to receive said first and second test datafrom said collection/input modes at a first and second stage operable toenable said learning mode associated with said operational or functionalcapability conditions of said known-good DUT during its service life; acontrol section operable to input said first and second test data intosaid multiple mode analysis decision engine to evaluate saidunknown-good DUT, wherein control section is adapted for integration ofsaid multiple EM sensors as well as said data inputs in synchronizationwith said plurality of DUT stimulation inputs to produce known-good DUTunique EM signatures usable by said decision engine to detect saidoperational or functional capability conditions of said known-good DUTin said unknown-good DUTs during said second stage; wherein said one ormore decision engines in said evaluating mode further evaluates multipleelectrical characteristic modalities data sets associated with saidoperational or functional capability conditions of said known-good DUTduring its service life with said second test data so as to determine aprobability that said unknown-good DUT is unauthorized, does not meetspecification(s), or is defective.
 8. A testing system as in claim 7,wherein said EM spectrum sensors and data collection sections comprise apositioning system, switch matrix, power combiner, switch and EMinterference (EMI) shielding to isolate or shield from EMI signals . 9.A testing system as in claim 8, wherein said EM sensors further compriseprobe types including one or more from the group comprising an E-fieldor H-field probe of varying bandwidths, visual, infra-red sensors.
 10. Atesting apparatus including systems for for measuring electromagneticemissions and determining defective components comprising: a sensorarray comprising a plurality of sensors adapted to be moveable; a signalanalysis section comprising a section comprising a time domain andsignal domain signal analysis signal section; a device under test (DUT)holder adapted to hold and position a first and second DUT relative tothe sensor array; a DUT stress application section adapted to generateone or more DUT stress conditions to said first DUT which correlate withlife cycle age or a life cycle reduction event associated with said DUT,where said DUT stress condition associated with life cycle age isapplied by an escalating series of burn-in or heating conditions appliedto said first DUT, said DUT life cycle reduction event compriseselectrostatic discharge application to at least one section of saidfirst DUT; a control mechanism adapted to independently positionelements of said sensor array relative to areas of interest on saidfirst and second DUT based on a first position input; a DUT controlsection comprising a machine readable storage medium adapted to receiveand store a plurality of machine readable instructions operable tocontrol said apparatus and said first and second DUTs, said machinereadable storage medium further comprises a first plurality of machinereadable instructions adapted to operate said control mechanism in orderto acquire and store a plurality of first sensor array signature dataassociated with said sensor array outputs from said first DUT based on afirst plurality of test signal control inputs to said first DUT and saidfirst position input while one or more said DUT stress conditions areapplied to said first DUT, wherein each of said plurality of said firstsensor array signature data is respectively associated with said one ormore DUT stress conditions; wherein said DUT control section furthercomprises a second plurality of machine readable instructions stored onsaid machine readable storage medium adapted to stimulate said secondDUT when said second DUT is placed in said DUT holder with said firstplurality of test signal control inputs, said DUT control sectionfurther comprises a third plurality of machine readable instructionsstored on said machine readable storage medium adapted to acquire aplurality of second sensor array signature data associated with saidsensor array outputs from said second DUT based on said first pluralityof test signal control inputs to said second DUT and said first positioninput; wherein said DUT control section further comprises a fourthplurality of machine readable instructions stored on said machinereadable storage medium adapted operate said apparatus to match saidfirst and second sensor array signature data associated respectivelywith said first and second DUT, wherein a substantial match of saidsignature data indicates a first condition associated with said secondDUT and a non-match indicates a second condition associated with saidsecond DUT; an input and output section adapted to interact with saidDUT control section, said input and output section comprising a userinterface including a graphical user interface adapted to display anindication of said first or second condition associated with said secondDUT; wherein said first condition comprises a DUT acceptable dataindicator and said second condition comprises a DUT unacceptable dataindicator; wherein said fourth plurality of machine readableinstructions comprises a decision engine and rule base comprising aplurality of data sets associated with a plurality of conditions ofinterest comprising said first and second conditions, said decisionengine and said rule base are operable for determining a probabilitythat said second DUT is associated with one or more of said plurality ofconditions, wherein said first condition comprises an authorized ormeets specification condition and said second condition comprises anunauthorized, does not meet specification, or is defective condition;wherein said system further comprises a section adapted to determinemultiple permutations of one or more said probability, said systemcomprises a control section configured for using said multiplepermutations to determine an overall probability of one or more saidconditions of interest which has a variety of attributes associated withone or more said DUT acceptance data and DUT rejection data; whereinsaid plurality of sensors comprise a plurality of DUT measurement anddata collection/input sections comprising electromagnetic (EM) spectrumsensors and data collection sections.
 11. A testing system including asystem for measuring electromagnetic emissions and determining defectivecomponents_comprising: a machine learning system means and a sensorsystem means adapted to test one or more devices under test (DUT) inlearning as well as evaluation modes, said sensor system means isadapted to collect a variety of test data as a part of said machinelearning system associated with a known-good said one or more DUTsamples subjected to simulated or generated condition of interestscomprising stimulation of sections of said known-good DUT andapplication of one or more stress events to said known-good DUT sample,said data collected by the machine learning system means is operable tocalculate probabilities data that an unknown-good DUT sample in anevaluation mode substantially matches a condition of interest comprisinga DUT sample acceptance data or a DUT rejection data; and an inputsection and an output section adapted to output said probabilities dataassociated with said unknown-good DUT; a control mechanism adapted toindependently position elements of said sensor system means relative toareas of interest on said one or more DUT samples including saidknown-good DUT and said unknown-good DUT samples based on at least afirst position input and data; wherein learning modes are switched onbefore, during, or after evaluation mode sequencing to improve or adjustmachine learning system capabilities to determine said probabilitiesassociated with different types of said conditions of interest whereinsaid system is configured to determine multiple permutations of saidprobabilities data to determine an overall probability of one or moresaid conditions of interest which has a variety of attributes associatedwith one or more said DUT acceptance data and DUT rejection data;wherein said learning and evaluation system means includes an artificialintelligence system comprising a neural network or a decision enginecomprising a plurality of rules associated with generating saidprobabilities data; wherein said learning and evaluation system meanscomprise a control section configured for determining a probability thatsaid DUT comprising a microelectronic device is unauthorized, does notmeet one or more elements of a specification data set, or is defective;wherein said sensor system means comprise a plurality of DUT measurementand data collection/input sections comprising electromagnetic (EM)spectrum sensors and data collection sections.
 12. A testing systemcomprising: a plurality of device under test (DUT) measurement and datacollection/input sections comprising electromagnetic (EM) spectrumsensors and data collection sections adapted to apply a plurality ofstimulation inputs to a plurality of points on a known-good and anunknown-good DUT and respectively sense a first and second plurality oftest data from said known-good and unknown good DUTs, wherein saidplurality of stimulation inputs applied to said known-good DUT isapplied during synchronized application of one or more life cyclesimulation stress environments are applied to said known-good DUTcomprising one or more of a group comprising electrostatic stress andageing events comprising burn-in or heating of said known-good DUTassociated with operational or functional capability conditions of saidknown-good DUT during specific points of said known-good DUT's servicelife; a control mechanism adapted to independently position elements ofsaid plurality of DUT measurement and data collection/input sectionsrelative to areas of interest on said known-good DUT and saidunknown-good DUT samples based on at least a first position input anddata stored within said testing system; one or more decision enginesections respectively associated with said plurality of DUT measurementand data collection/input sections, said one or more decision enginesections comprising a neural network, image recognition, statisticalcorrelation section, and decision tree section, said one or moredecision engines adapted to operate said DUT in a learning mode and anevaluating mode, said one or more decision engines are further adaptedto receive said first and second test data from said collection/inputmodes at a first and second stage operable to enable said learning modeassociated with said operational or functional capability conditions ofsaid known-good DUT during its service life; a control section operableto input said first and second test data into said multiple modeanalysis decision engine to evaluate said unknown-good DUT, whereincontrol section is adapted for integration of said multiple EM sensorsas well as said data inputs in synchronization with said plurality ofDUT stimulation inputs to produce known-good DUT unique EM signaturesusable by said decision engine to detect said operational or functionalcapability conditions of said known-good DUT in said unknown-good DUTsduring said second stage; wherein said one or more decision engines insaid evaluating mode further evaluates multiple electricalcharacteristic modalities data sets associated with said operational orfunctional capability conditions of said known-good DUT during itsservice life with said second test data so as to determine and store oroutput a probability data that said unknown-good DUT is unauthorized,does not meet specification data stored within said system, or isdefective; wherein said EM spectrum sensors and data collection sectionscomprise a positioning system, switch matrix, power combiner, switch andEM interference (EMI) shielding to isolate or shield from EMI signals;wherein said EM sensors further comprise probe types including one ormore from the group comprising an E-field or H-field probe of varyingbandwidths, visual, infra-red sensors; wherein said one or more decisionengines in said evaluating mode further comprises a control sectionconfigured for determining and storing or outputting a secondprobability data that said unknown-good DUT comprising a microelectronicdevice is unauthorized, does not meet one or more elements of aspecification data set, or is defective.